Geometric Knowledge Embedding for unsupervised domain adaptation

Abstract

Domain adaptation aims to transfer auxiliary knowledge from a source domain to enhance the learning performance on a target domain. Recent studies have suggested that deep networks are able to achieve promising results for domain adaptation problems. However, deep neural networks cannot reveal the underlying geometric information from input data. Indeed, such geometric information is very useful for describing the relationship between the samples from source and target domains. In this paper, we propose a novel learning algorithm named GKE, which stands for Geometric Knowledge Embedding. In GKE, we use a graph-based model to explore the underlying geometric structure of the input source and target data based on their similarities. Concretely, we develop a graph convolutional network to learn discriminative representations based on the constructed graph. To obtain effective transferable representations, we match source and target domains by reducing the Maximum Mean Discrepancy (MMD) between their learned representations. Extensive experiments on real-world data sets demonstrate that the proposed method outperforms existing domain adaption methods.